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BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology Independent and Interoperable Modeling of Machine Learning Workflows and their Serverless Deployment Orchestration
Software Engineering · Submitted 2 Aug 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle.
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The abstract, sentence by sentence
Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. However, it is short of specific support to represent machine learning workflows. Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. Research is needed to align the process from modeling to deploying ML workflows. We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. It aids in arriving at a shared and standardized language to communicate ML solutions. Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Machine learning (ML) continues to permeate all layers of academia, industry and society. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. | gap 0.96 | 0.12 | 0.03 | asserted 1.00 |
| s2 | For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s3 | However, it is short of specific support to represent machine learning workflows. | gap 0.79 | 0.11 | 0.02 | asserted 1.00 |
| s4 | Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. | background 0.81 | 0.07 | 0.02 | asserted 0.69 |
| s5 | Research is needed to align the process from modeling to deploying ML workflows. | gap 0.69 | 0.05 | 0.04 | asserted 0.97 |
| s6 | We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. | contribution 0.93 | 0.92 | 0.05 | asserted 0.94 |
| s7 | Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). | contribution 0.99 | 0.96 | 0.29 | asserted 1.00 |
| s8 | Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. | method 0.73 | 0.75 | 0.05 | asserted 1.00 |
| s9 | We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. | contribution 0.75 | 0.90 | 0.10 | asserted 0.99 |
| s10 | BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. | contribution 0.75 | 0.77 | 0.59 | asserted 1.00 |
| s11 | It aids in arriving at a shared and standardized language to communicate ML solutions. | interpretation 0.73 | 0.44 | 0.16 | asserted 0.88 |
| s12 | Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA. | contribution 0.51 | 0.83 | 0.24 | asserted 0.67 |
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The paper
Original abstract
Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. However, it is short of specific support to represent machine learning workflows. Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. Research is needed to align the process from modeling to deploying ML workflows. We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. It aids in arriving at a shared and standardized language to communicate ML solutions. Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA.
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